scholarly journals Vapour Diffusion Open Arctic Wall: A Comparison of Moisture Accumulation Potential Versus Other Cellulose Superinsulation Strategies

2021 ◽  
Author(s):  
Cory Brun
Keyword(s):  
Energy Efficient ◽  
Potential Versus ◽  
Experimental Simulation ◽  
The Arctic ◽  
Air Leakage ◽  
Energy Retrofit ◽  
Building Enclosure ◽  
Vapour Diffusion ◽  
Heating Load ◽  
Open Materials

Superinsulation is becoming increasingly attractive in the construction of energy efficient new homes or energy retrofit projects. By increasing the thermal insulation inside walls, new possible unforeseen building durability issues arise that were otherwise not present during standard 2”x6” construction, as there is less potential for drying. The reduced drying is often attributed to using low permeance materials in the building enclosure. One method to combat the reduced drying potential is to use the highest permeable vapour diffusion open materials for all building enclosure components such as the “Arctic Wall”. The purpose of this study is to determine how the Arctic Wall performs in Fairbanks, Alaska in addition to other climates, and how it also compares with other common vapour diffusion open methods. The results of experimental simulation using WUFI 5.2 hygrothermal software have shown that all vapour diffusion open walls have a potential for condensation that is most dominated by the heating load across the climates that were tested. The Arctic Wall was found to be safe to use in all climates using the tested methods, but still poses a potential risk due to potential condensation due to air leakage. The results of this study have shown that the Arctic Wall performed on par with other vapour diffusion open strategies.

scholarly journals Vapour Diffusion Open Arctic Wall: A Comparison of Moisture Accumulation Potential Versus Other Cellulose Superinsulation Strategies

2021 ◽  
Author(s):  
Cory Brun
Keyword(s):  
Energy Efficient ◽  
Potential Versus ◽  
Experimental Simulation ◽  
The Arctic ◽  
Air Leakage ◽  
Energy Retrofit ◽  
Building Enclosure ◽  
Vapour Diffusion ◽  
Heating Load ◽  
Open Materials

Superinsulation is becoming increasingly attractive in the construction of energy efficient new homes or energy retrofit projects. By increasing the thermal insulation inside walls, new possible unforeseen building durability issues arise that were otherwise not present during standard 2”x6” construction, as there is less potential for drying. The reduced drying is often attributed to using low permeance materials in the building enclosure. One method to combat the reduced drying potential is to use the highest permeable vapour diffusion open materials for all building enclosure components such as the “Arctic Wall”. The purpose of this study is to determine how the Arctic Wall performs in Fairbanks, Alaska in addition to other climates, and how it also compares with other common vapour diffusion open methods. The results of experimental simulation using WUFI 5.2 hygrothermal software have shown that all vapour diffusion open walls have a potential for condensation that is most dominated by the heating load across the climates that were tested. The Arctic Wall was found to be safe to use in all climates using the tested methods, but still poses a potential risk due to potential condensation due to air leakage. The results of this study have shown that the Arctic Wall performed on par with other vapour diffusion open strategies.

Must attic ventilation be preserved in energy retrofits?

2019 ◽  
Vol 37 (4) ◽  
pp. 461-472
Author(s):  
William Rose
Keyword(s):  
Vapor Pressure ◽  
Energy Efficient ◽  
Design Methodology ◽  
Air Leakage ◽  
Content Type ◽  
Buoyancy Flow ◽  
Spreadsheet Model ◽  
Radiation Exchange ◽  
Moisture Performance ◽  
Ashrae Standards

Purpose The addition of thermal insulation into attics along with air-tightening of the ceiling plane is a common first step in making homes more energy efficient. Attic ventilation was introduced decades ago on the assumption that air leakage across the ceiling was inevitable and not correctible – this was before the era of spray-applied foams. Often attic ventilation is provided at roof eaves, and ensuring good insulation in their location is critical to avoid cold corners in the rooms below. So may vents be blocked in the course of energy retrofits? The paper aims to discuss this issue. Design/methodology/approach This study consists of a simple spreadsheet model of attic performance. The model is built using material from ASHRAE Handbook Fundamentals and ASHRAE Standards. It includes: Glaser calculations for temperature, vapor pressure and vapor pressure excess; radiation exchange – solar and sky; buoyancy flow assumption for leakage from indoors; wind flow assumption for leakage from outdoors; and change in attic air RH as assumed indicator of change in sheathing moisture performance. Findings The model results show that lowered moisture contributions across air-tightened ceilings may compensate effectively for added insulation (which lowers the attic air temperature) and reduced moisture dilution from attic ventilation. Originality/value These results provide support for the policy of allowing attic ventilation reductions that are proportionate to ceiling air leakage reductions as part of weatherization efforts. Given the limitations of the model, continued field observations remain critical.

Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments

2016 ◽  
Vol 819 ◽  
pp. 541-545 ◽  
Author(s):  
Sholahudin ◽  
Azimil Gani Alam ◽  
Chang In Baek ◽  
Hwataik Han
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Residential Buildings ◽  
Building Energy ◽  
Cooling Load ◽  
Simulation Data ◽  
Heating And Cooling ◽  
Heating Load ◽  
Input Variables ◽  
Cooling Loads

Energy consumption of buildings is increasing steadily and occupying approximately 30-40% of total energy use. It is important to predict heating and cooling loads of a building in the initial stage of design to find out optimal solutions among various design options, as well as in the operating stage after the building has been completed for energy efficient operation. In this paper, an artificial neural network model has been developed to predict heating and cooling loads of a building based on simulation data for building energy performance. The input variables include relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a building, and the output variables include heating load (HL) and cooling load (CL) of the building. The simulation data used for training are the data published in the literature for various 768 residential buildings. ANNs have a merit in estimating output values for given input values satisfactorily, but it has a limitation in acquiring the effects of input variables individually. In order to analyze the effects of the variables, we used a method for design of experiment and conducted ANOVA analysis. The sensitivities of individual variables have been investigated and the most energy efficient solution has been estimated under given conditions. Discussions are included in the paper regarding the variables affecting heating load and cooling load significantly and the effects on heating and cooling loads of residential buildings.

scholarly journals Highly Insulated Wall Systems with Exterior Insulation of Polyisocyanurate under Different Facer Materials: Material Characterization and Long-Term Hygrothermal Performance Assessment

Materials ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3373 ◽  
Author(s):  
Emishaw Iffa ◽  
Fitsum Tariku ◽  
Wendy Ying Simpson
Keyword(s):  
Performance Assessment ◽  
Control Strategy ◽  
Energy Efficient ◽  
Material Characterization ◽  
Air Leakage ◽  
Material Development ◽  
Hygrothermal Performance ◽  
New Construction ◽  
Wood Frame

The application of exterior insulation in both new construction and retrofits is a common practice to enhance the energy efficiency of buildings. In addition to increased thermal performance, the rigid insulation can serve to keep the sheathing board warm and serve as a water-resistive barrier to keep moisture-related problems due to condensation and wind-driven rain. Polyisocyanurate (PIR) rigid boards have a higher thermal resistance in comparison to other commonly used exterior insulation boards. However, because of its perceived lower permeance, its use as exterior insulation is not very common. In this study, the hygrothermal property of PIR boards with different facer types and thicknesses is characterized. The material data obtained through experimental test and extrapolation is used in a long term hygrothermal performance assessment of a wood frame wall with PIR boards as exterior insulation. Results show that PIR with no facer has the smallest accumulated moisture on the sheathing board in comparison to other insulation boards. Walls with a bigger thickness of exterior insulation perform better when no vapor barrier is used. The PIR exterior insulation supports the moisture control strategy well in colder climates in perfect wall scenarios, where there is no air leakage and moisture intrusion. In cases where there is trapped moisture, the sheathing board has a higher moisture content with PIR boards with both aluminum or fiberglass type facers. An innovative facer material development for PIR boards can help efforts targeting improved energy-efficient and durable wall systems.

scholarly journals Case Studies Assessment Report

2021 ◽  
Author(s):  
Daniel Herrera ◽  
Franziska Haas ◽  
Alexandra Troi ◽  
Gustaf Leijonhufvud ◽  
Tor Broström ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Knowledge Base ◽  
Case Studies ◽  
Energy Efficient ◽  
Building Energy ◽  
Historic Buildings ◽  
Historic Building ◽  
Assessment Report ◽  
Energy Retrofit

One of the main targets of SHC Task 59 is to provide a solid knowledge base on deep renovation of historic buildings. The Historic Building Energy Retrofit Atlas (HiBERatlas, www.hiberatlas.com) provides a bestpractice database of exemplary energy efficient  interventions in historic buildings. The database presents bestpractice examples of how a historic building can be renovated to achieve high levels of energy efficiency while respecting and protecting its heritage significance.

scholarly journals Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques

2019 ◽  
Vol 9 (20) ◽  
pp. 4338 ◽  
Author(s):  
Hossein Moayedi ◽  
Dieu Tien Bui ◽  
Anastasios Dounis ◽  
Zongjie Lyu ◽  
Loke Kok Foong
Keyword(s):  
Machine Learning ◽  
Energy Efficient ◽  
Design Procedure ◽  
Absolute Error ◽  
Energy Performance ◽  
Machine Learning Techniques ◽  
Energy Efficient Buildings ◽  
Squared Error ◽  
Learning Techniques ◽  
Heating Load

The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R2), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings.

Sign in / Sign up

Export Citation Format

Share Document